Class-incremental Learning (CIL) enables the model to incrementally absorb knowledge from new classes and build a generic classifier across all previously encountered classes. When the model optimizes with new classes, the knowledge of previous classes is inevitably erased, leading to catastrophic forgetting. Addressing this challenge requires making a trade-off between retaining old knowledge and accommodating new information. However, this balancing process often requires sacrificing some information, which can lead to a partial loss in the model's ability to discriminate between classes. To tackle this issue, we design the adaptive weighted parameter fusion with Contrastive Language-Image Pre-training (CLIP), which not only takes into account the variability of the data distribution of different tasks, but also retains all the effective information of the parameter matrix to the greatest extent. In addition, we introduce a balance factor that can balance the data distribution alignment and distinguishability of adjacent tasks. Experimental results on several traditional benchmarks validate the superiority of the proposed method.

翻译：类增量学习（CIL）使模型能够逐步吸收新类别的知识，并构建一个涵盖所有已见类别的通用分类器。当模型针对新类别进行优化时，先前类别的知识不可避免地会被擦除，从而导致灾难性遗忘。应对这一挑战需要在保留旧知识与适应新信息之间做出权衡。然而，这种平衡过程通常需要牺牲部分信息，这可能导致模型在类别区分能力上的部分损失。为解决这一问题，我们设计了基于对比语言-图像预训练（CLIP）的自适应加权参数融合方法，该方法不仅考虑了不同任务数据分布的差异性，还最大限度地保留了参数矩阵的全部有效信息。此外，我们引入了一个平衡因子，能够权衡相邻任务间的数据分布对齐性与可区分性。在多个传统基准测试上的实验结果验证了所提方法的优越性。